MétaCan
Menu
Back to cohort
Record W2072130210 · doi:10.1504/writr.2010.031584

An efficiency study of airlines and air cargo/passenger divisions: a DEA approach

2010· article· en· W2072130210 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld Review of Intermodal Transportation Research · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsData envelopment analysisBusinessAir cargoAir transportCarry (investment)Industrial organizationOperational efficiencyTransport engineeringOperations researchMarketingEngineeringFinanceMathematicsStatistics

Abstract

fetched live from OpenAlex

In this paper we investigate the question of whether a high degree of cargo business improves the operational efficiency of a mixed passenger/cargo airline. We use Data Envelopment Analysis (DEA) to compute the efficiency scores for major airlines in the world and then carry out non-parametric statistical tests. Using data for 29 airlines during the period 1998-2002, we find that airlines with a high share of cargo business in their overall operations are significantly more efficient than airlines with a low share of cargo business. On the other hand, no statistically significant differences are found between airlines with similar degrees of cargo business. Managerial and policy implications of our results are also discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.488
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it